Overview

Dataset statistics

Number of variables22
Number of observations1311
Missing cells1065
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.5 KiB
Average record size in memory176.1 B

Variable types

Categorical8
Numeric14

Warnings

Uniformity has constant value "10.0" Constant
Clean.Cup has constant value "10.0" Constant
Sweetness has constant value "10.0" Constant
Category.One.Defects has constant value "0.0" Constant
Quakers has constant value "0.0" Constant
Producer has a high cardinality: 675 distinct values High cardinality
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
Clean.Cup is highly correlated with Sweetness and 5 other fieldsHigh correlation
Sweetness is highly correlated with Clean.Cup and 5 other fieldsHigh correlation
Processing Method is highly correlated with Clean.Cup and 4 other fieldsHigh correlation
Quakers is highly correlated with Clean.Cup and 5 other fieldsHigh correlation
Country of Origin is highly correlated with Clean.Cup and 4 other fieldsHigh correlation
Uniformity is highly correlated with Clean.Cup and 5 other fieldsHigh correlation
Category.One.Defects is highly correlated with Clean.Cup and 5 other fieldsHigh correlation
Producer has 230 (17.5%) missing values Missing
Processing Method has 152 (11.6%) missing values Missing
altitude_low_meters has 227 (17.3%) missing values Missing
altitude_high_meters has 227 (17.3%) missing values Missing
altitude_mean_meters has 227 (17.3%) missing values Missing
Category.Two.Defects has 362 (27.6%) zeros Zeros

Reproduction

Analysis started2021-02-14 14:04:20.286814
Analysis finished2021-02-14 14:04:44.852847
Duration24.57 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Country of Origin
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Memory size10.4 KiB
Mexico
236 
Colombia
183 
Guatemala
181 
Brazil
132 
Taiwan
75 
Other values (31)
503 

Length

Max length28
Median length8
Mean length8.893129771
Min length4

Characters and Unicode

Total characters11650
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.6%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia
ValueCountFrequency (%)
Mexico236
18.0%
Colombia183
14.0%
Guatemala181
13.8%
Brazil132
10.1%
Taiwan75
 
5.7%
United States (Hawaii)73
 
5.6%
Honduras53
 
4.0%
Costa Rica51
 
3.9%
Ethiopia44
 
3.4%
Tanzania, United Republic Of40
 
3.1%
Other values (26)242
18.5%
2021-02-14T15:04:45.045905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico236
14.1%
colombia183
 
11.0%
guatemala181
 
10.8%
brazil132
 
7.9%
united125
 
7.5%
states85
 
5.1%
taiwan75
 
4.5%
hawaii73
 
4.4%
honduras53
 
3.2%
costa51
 
3.1%
Other values (35)477
28.5%

Most occurring characters

ValueCountFrequency (%)
a1933
16.6%
i1267
 
10.9%
o805
 
6.9%
e742
 
6.4%
l626
 
5.4%
t590
 
5.1%
n502
 
4.3%
m384
 
3.3%
361
 
3.1%
c358
 
3.1%
Other values (37)4082
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9423
80.9%
Uppercase Letter1671
 
14.3%
Space Separator361
 
3.1%
Open Punctuation77
 
0.7%
Close Punctuation77
 
0.7%
Other Punctuation41
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
a1933
20.5%
i1267
13.4%
o805
8.5%
e742
 
7.9%
l626
 
6.6%
t590
 
6.3%
n502
 
5.3%
m384
 
4.1%
c358
 
3.8%
u323
 
3.4%
Other values (13)1893
20.1%
ValueCountFrequency (%)
M256
15.3%
C251
15.0%
G182
10.9%
U151
9.0%
T147
8.8%
B134
8.0%
H132
7.9%
S106
6.3%
R96
 
5.7%
E66
 
3.9%
Other values (9)150
9.0%
ValueCountFrequency (%)
,40
97.6%
?1
 
2.4%
ValueCountFrequency (%)
361
100.0%
ValueCountFrequency (%)
(77
100.0%
ValueCountFrequency (%)
)77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11094
95.2%
Common556
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
a1933
17.4%
i1267
 
11.4%
o805
 
7.3%
e742
 
6.7%
l626
 
5.6%
t590
 
5.3%
n502
 
4.5%
m384
 
3.5%
c358
 
3.2%
u323
 
2.9%
Other values (32)3564
32.1%
ValueCountFrequency (%)
361
64.9%
(77
 
13.8%
)77
 
13.8%
,40
 
7.2%
?1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11650
100.0%

Most frequent character per block

ValueCountFrequency (%)
a1933
16.6%
i1267
 
10.9%
o805
 
6.9%
e742
 
6.4%
l626
 
5.4%
t590
 
5.1%
n502
 
4.3%
m384
 
3.3%
361
 
3.1%
c358
 
3.1%
Other values (37)4082
35.0%

Producer
Categorical

HIGH CARDINALITY
MISSING

Distinct675
Distinct (%)62.4%
Missing230
Missing (%)17.5%
Memory size10.4 KiB
La Plata
 
30
Ipanema Agrícola SA
 
22
Doi Tung Development Project
 
17
Ipanema Agricola
 
12
VARIOS
 
12
Other values (670)
988 

Length

Max length100
Median length19
Mean length20.54024052
Min length1

Characters and Unicode

Total characters22204
Distinct characters222
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique526 ?
Unique (%)48.7%

Sample

1st rowMETAD PLC
2nd rowMETAD PLC
3rd rowYidnekachew Dabessa Coffee Plantation
4th rowMETAD PLC
5th rowHVC
ValueCountFrequency (%)
La Plata30
 
2.3%
Ipanema Agrícola SA22
 
1.7%
Doi Tung Development Project17
 
1.3%
Ipanema Agricola12
 
0.9%
VARIOS12
 
0.9%
Ipanema Agricola S.A11
 
0.8%
ROBERTO MONTERROSO10
 
0.8%
AMILCAR LAPOLA9
 
0.7%
Reinerio Zepeda9
 
0.7%
LA PLATA9
 
0.7%
Other values (665)940
71.7%
(Missing)230
 
17.5%
2021-02-14T15:04:45.292339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de86
 
2.5%
65
 
1.9%
coffee64
 
1.9%
la60
 
1.8%
ipanema50
 
1.5%
s.a50
 
1.5%
plata41
 
1.2%
agricola37
 
1.1%
ltd33
 
1.0%
sa30
 
0.9%
Other values (1241)2902
84.9%

Most occurring characters

ValueCountFrequency (%)
2392
 
10.8%
A1473
 
6.6%
a1174
 
5.3%
R962
 
4.3%
E961
 
4.3%
e902
 
4.1%
O886
 
4.0%
o824
 
3.7%
I758
 
3.4%
L674
 
3.0%
Other values (212)11198
50.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10990
49.5%
Lowercase Letter8057
36.3%
Space Separator2392
 
10.8%
Other Punctuation373
 
1.7%
Other Letter233
 
1.0%
Decimal Number99
 
0.4%
Dash Punctuation17
 
0.1%
Open Punctuation14
 
0.1%
Close Punctuation14
 
0.1%
Math Symbol12
 
0.1%
Other values (2)3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
a1174
14.6%
e902
11.2%
o824
10.2%
n608
 
7.5%
r607
 
7.5%
i554
 
6.9%
l392
 
4.9%
t392
 
4.9%
s328
 
4.1%
u322
 
4.0%
Other values (24)1954
24.3%
ValueCountFrequency (%)
A1473
13.4%
R962
 
8.8%
E961
 
8.7%
O886
 
8.1%
I758
 
6.9%
L674
 
6.1%
N620
 
5.6%
S610
 
5.6%
C601
 
5.5%
M430
 
3.9%
Other values (22)3015
27.4%
ValueCountFrequency (%)
020
20.2%
220
20.2%
116
16.2%
312
12.1%
911
11.1%
48
 
8.1%
65
 
5.1%
73
 
3.0%
52
 
2.0%
82
 
2.0%
ValueCountFrequency (%)
.195
52.3%
,95
25.5%
/53
 
14.2%
&13
 
3.5%
'6
 
1.6%
:6
 
1.6%
;5
 
1.3%
ValueCountFrequency (%)
2392
100.0%
ValueCountFrequency (%)
|12
100.0%
ValueCountFrequency (%)
-17
100.0%
ValueCountFrequency (%)
(14
100.0%
ValueCountFrequency (%)
)14
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19047
85.8%
Common2924
 
13.2%
Han233
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
A1473
 
7.7%
a1174
 
6.2%
R962
 
5.1%
E961
 
5.0%
e902
 
4.7%
O886
 
4.7%
o824
 
4.3%
I758
 
4.0%
L674
 
3.5%
N620
 
3.3%
Other values (56)9813
51.5%
ValueCountFrequency (%)
2392
81.8%
.195
 
6.7%
,95
 
3.2%
/53
 
1.8%
020
 
0.7%
220
 
0.7%
-17
 
0.6%
116
 
0.5%
(14
 
0.5%
)14
 
0.5%
Other values (14)88
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21867
98.5%
CJK233
 
1.0%
None102
 
0.5%
Punctuation2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2392
 
10.9%
A1473
 
6.7%
a1174
 
5.4%
R962
 
4.4%
E961
 
4.4%
e902
 
4.1%
O886
 
4.1%
o824
 
3.8%
I758
 
3.5%
L674
 
3.1%
Other values (65)10861
49.7%
ValueCountFrequency (%)
í25
24.5%
é13
12.7%
ó12
11.8%
Ñ10
 
9.8%
Í9
 
8.8%
ú7
 
6.9%
á6
 
5.9%
Ó4
 
3.9%
É4
 
3.9%
è4
 
3.9%
Other values (4)8
 
7.8%
ValueCountFrequency (%)
12
 
5.2%
7
 
3.0%
6
 
2.6%
5
 
2.1%
5
 
2.1%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (122)176
75.5%
ValueCountFrequency (%)
2
100.0%

Number.of.Bags
Real number (ℝ≥0)

Distinct129
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.2112891
Minimum0
Maximum600
Zeros1
Zeros (%)0.1%
Memory size10.4 KiB
2021-02-14T15:04:45.412737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114.5
median175
Q3275
95-th percentile320
Maximum600
Range600
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation127.28396
Coefficient of variation (CV)0.8307740293
Kurtosis-1.441537985
Mean153.2112891
Median Absolute Deviation (MAD)125
Skewness0.08097133978
Sum200860
Variance16201.20647
MonotocityNot monotonic
2021-02-14T15:04:45.528234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250241
18.4%
275176
13.4%
10108
 
8.2%
187
 
6.6%
30071
 
5.4%
32070
 
5.3%
5042
 
3.2%
10037
 
2.8%
2035
 
2.7%
229
 
2.2%
Other values (119)415
31.7%
ValueCountFrequency (%)
01
 
0.1%
187
6.6%
229
 
2.2%
318
 
1.4%
46
 
0.5%
ValueCountFrequency (%)
6001
 
0.1%
5502
0.2%
5002
0.2%
4502
0.2%
4403
0.2%

Processing Method
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.4%
Missing152
Missing (%)11.6%
Memory size10.4 KiB
Washed / Wet
812 
Natural / Dry
251 
Semi-washed / Semi-pulped
 
56
Other
 
26
Pulped natural / honey
 
14

Length

Max length25
Median length12
Mean length12.80845557
Min length5

Characters and Unicode

Total characters14845
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry
ValueCountFrequency (%)
Washed / Wet812
61.9%
Natural / Dry251
 
19.1%
Semi-washed / Semi-pulped56
 
4.3%
Other26
 
2.0%
Pulped natural / honey14
 
1.1%
(Missing)152
 
11.6%
2021-02-14T15:04:45.756920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:45.823345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1133
32.9%
washed812
23.6%
wet812
23.6%
natural265
 
7.7%
dry251
 
7.3%
semi-washed56
 
1.6%
semi-pulped56
 
1.6%
other26
 
0.8%
pulped14
 
0.4%
honey14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2280
15.4%
e1902
12.8%
W1624
10.9%
a1398
9.4%
/1133
7.6%
t1103
7.4%
d938
6.3%
h908
 
6.1%
s868
 
5.8%
r542
 
3.7%
Other values (15)2149
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9042
60.9%
Space Separator2280
 
15.4%
Uppercase Letter2278
 
15.3%
Other Punctuation1133
 
7.6%
Dash Punctuation112
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
e1902
21.0%
a1398
15.5%
t1103
12.2%
d938
10.4%
h908
10.0%
s868
9.6%
r542
 
6.0%
u335
 
3.7%
l335
 
3.7%
y265
 
2.9%
Other values (6)448
 
5.0%
ValueCountFrequency (%)
W1624
71.3%
N251
 
11.0%
D251
 
11.0%
S112
 
4.9%
O26
 
1.1%
P14
 
0.6%
ValueCountFrequency (%)
2280
100.0%
ValueCountFrequency (%)
/1133
100.0%
ValueCountFrequency (%)
-112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11320
76.3%
Common3525
 
23.7%

Most frequent character per script

ValueCountFrequency (%)
e1902
16.8%
W1624
14.3%
a1398
12.3%
t1103
9.7%
d938
8.3%
h908
8.0%
s868
7.7%
r542
 
4.8%
u335
 
3.0%
l335
 
3.0%
Other values (12)1367
12.1%
ValueCountFrequency (%)
2280
64.7%
/1133
32.1%
-112
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII14845
100.0%

Most frequent character per block

ValueCountFrequency (%)
2280
15.4%
e1902
12.8%
W1624
10.9%
a1398
9.4%
/1133
7.6%
t1103
7.4%
d938
6.3%
h908
 
6.1%
s868
 
5.8%
r542
 
3.7%
Other values (15)2149
14.5%

Aroma
Real number (ℝ≥0)

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.574912281
Minimum7
Maximum8.17
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:45.898618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.17
Q17.42
median7.58
Q37.75
95-th percentile8
Maximum8.17
Range1.17
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.2446130531
Coefficient of variation (CV)0.03229252617
Kurtosis-0.1632119554
Mean7.574912281
Median Absolute Deviation (MAD)0.16
Skewness-0.0215703415
Sum9930.71
Variance0.05983554573
MonotocityNot monotonic
2021-02-14T15:04:45.987404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.58219
16.7%
7.67173
13.2%
7.5163
12.4%
7.75122
9.3%
7.42121
9.2%
7.83101
7.7%
7.3396
7.3%
7.2578
 
5.9%
7.9257
 
4.3%
7.1745
 
3.4%
Other values (6)136
10.4%
ValueCountFrequency (%)
723
 
1.8%
7.0828
 
2.1%
7.1745
3.4%
7.2578
5.9%
7.3396
7.3%
ValueCountFrequency (%)
8.1720
 
1.5%
8.0820
 
1.5%
843
3.3%
7.9257
4.3%
7.83101
7.7%

Flavor
Real number (ℝ≥0)

Distinct22
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.532379863
Minimum6.75
Maximum8.33
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:46.072460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.75
5-th percentile7
Q17.33
median7.58
Q37.75
95-th percentile8
Maximum8.33
Range1.58
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.2858398297
Coefficient of variation (CV)0.03794814321
Kurtosis0.2020794817
Mean7.532379863
Median Absolute Deviation (MAD)0.17
Skewness-0.1877976558
Sum9874.95
Variance0.08170440827
MonotocityNot monotonic
2021-02-14T15:04:46.171927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7.58205
15.6%
7.5164
12.5%
7.67145
11.1%
7.75120
9.2%
7.42114
8.7%
7.33110
8.4%
7.8385
6.5%
7.2564
 
4.9%
7.1756
 
4.3%
7.0842
 
3.2%
Other values (12)206
15.7%
ValueCountFrequency (%)
6.7510
 
0.8%
6.8316
 
1.2%
6.9215
 
1.1%
736
2.7%
7.0842
3.2%
ValueCountFrequency (%)
8.335
 
0.4%
8.257
 
0.5%
8.1718
1.4%
8.0813
 
1.0%
841
3.1%

Aftertaste
Real number (ℝ≥0)

Distinct18
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.420823799
Minimum6.83
Maximum8
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:46.262813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.83
5-th percentile6.92
Q17.25
median7.42
Q37.58
95-th percentile7.83
Maximum8
Range1.17
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.2636373728
Coefficient of variation (CV)0.03552669892
Kurtosis-0.3004776209
Mean7.420823799
Median Absolute Deviation (MAD)0.17
Skewness-0.146133478
Sum9728.7
Variance0.06950466435
MonotocityNot monotonic
2021-02-14T15:04:46.360309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7.42212
16.2%
7.5162
12.4%
7.33150
11.4%
7.58125
9.5%
7.25103
7.9%
7.6799
7.6%
7.1790
6.9%
7.7581
 
6.2%
762
 
4.7%
7.8361
 
4.7%
Other values (8)166
12.7%
ValueCountFrequency (%)
6.8336
 
2.7%
6.9236
 
2.7%
762
4.7%
7.0845
3.4%
7.1790
6.9%
ValueCountFrequency (%)
827
 
2.1%
7.9219
 
1.4%
7.881
 
0.1%
7.8361
4.7%
7.7581
6.2%

Acidity
Real number (ℝ≥0)

Distinct21
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.53518688
Minimum6.75
Maximum8.33
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:46.449063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.75
5-th percentile7.08
Q17.33
median7.5
Q37.75
95-th percentile8
Maximum8.33
Range1.58
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.2850339865
Coefficient of variation (CV)0.03782706269
Kurtosis0.057615902
Mean7.53518688
Median Absolute Deviation (MAD)0.17
Skewness0.03306875361
Sum9878.63
Variance0.08124437345
MonotocityNot monotonic
2021-02-14T15:04:46.546400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7.5185
14.1%
7.58150
11.4%
7.67143
10.9%
7.42127
9.7%
7.75122
9.3%
7.33110
8.4%
7.2586
6.6%
7.1773
 
5.6%
7.8372
 
5.5%
847
 
3.6%
Other values (11)196
15.0%
ValueCountFrequency (%)
6.756
 
0.5%
6.8311
 
0.8%
6.9210
 
0.8%
732
2.4%
7.0836
2.7%
ValueCountFrequency (%)
8.339
 
0.7%
8.256
 
0.5%
8.1714
 
1.1%
8.0825
1.9%
847
3.6%

Body
Real number (ℝ≥0)

Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.520213577
Minimum6.83
Maximum8.17
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:46.637058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.83
5-th percentile7.08
Q17.33
median7.5
Q37.67
95-th percentile7.92
Maximum8.17
Range1.34
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.2553470026
Coefficient of variation (CV)0.03395475408
Kurtosis-0.2376636121
Mean7.520213577
Median Absolute Deviation (MAD)0.17
Skewness-0.02666375812
Sum9859
Variance0.06520209175
MonotocityNot monotonic
2021-02-14T15:04:46.732639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.5228
17.4%
7.67149
11.4%
7.58136
10.4%
7.33131
10.0%
7.42125
9.5%
7.75108
8.2%
7.2586
 
6.6%
7.8382
 
6.3%
7.1768
 
5.2%
7.9248
 
3.7%
Other values (9)150
11.4%
ValueCountFrequency (%)
6.834
 
0.3%
6.9211
 
0.8%
734
2.6%
7.0837
2.8%
7.1768
5.2%
ValueCountFrequency (%)
8.177
 
0.5%
8.0821
 
1.6%
834
2.6%
7.9248
3.7%
7.8382
6.3%

Balance
Real number (ℝ≥0)

Distinct20
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.517307399
Minimum6.75
Maximum8.33
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:46.819120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.75
5-th percentile7
Q17.33
median7.5
Q37.75
95-th percentile8
Maximum8.33
Range1.58
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.300968659
Coefficient of variation (CV)0.04003676356
Kurtosis-0.04027101873
Mean7.517307399
Median Absolute Deviation (MAD)0.17
Skewness-0.1047857
Sum9855.19
Variance0.09058213368
MonotocityNot monotonic
2021-02-14T15:04:46.917306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7.5211
16.1%
7.67145
11.1%
7.58127
9.7%
7.42120
9.2%
7.75103
7.9%
7.3399
7.6%
7.8398
7.5%
7.1771
 
5.4%
7.2564
 
4.9%
746
 
3.5%
Other values (10)227
17.3%
ValueCountFrequency (%)
6.757
 
0.5%
6.8322
1.7%
6.9226
2.0%
746
3.5%
7.0841
3.1%
ValueCountFrequency (%)
8.337
 
0.5%
8.258
 
0.6%
8.1717
 
1.3%
8.0816
 
1.2%
845
3.4%

Uniformity
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
10.0
1311 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5244
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0
ValueCountFrequency (%)
10.01311
100.0%
2021-02-14T15:04:47.096493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:47.147534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
10.01311
100.0%

Most occurring characters

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3933
75.0%
Other Punctuation1311
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
02622
66.7%
11311
33.3%
ValueCountFrequency (%)
.1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5244
100.0%

Most frequent character per script

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5244
100.0%

Most frequent character per block

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Clean.Cup
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
10.0
1311 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5244
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0
ValueCountFrequency (%)
10.01311
100.0%
2021-02-14T15:04:47.294326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:47.345500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
10.01311
100.0%

Most occurring characters

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3933
75.0%
Other Punctuation1311
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
02622
66.7%
11311
33.3%
ValueCountFrequency (%)
.1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5244
100.0%

Most frequent character per script

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5244
100.0%

Most frequent character per block

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Sweetness
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
10.0
1311 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5244
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0
ValueCountFrequency (%)
10.01311
100.0%
2021-02-14T15:04:47.491196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:47.542499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
10.01311
100.0%

Most occurring characters

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3933
75.0%
Other Punctuation1311
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
02622
66.7%
11311
33.3%
ValueCountFrequency (%)
.1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5244
100.0%

Most frequent character per script

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5244
100.0%

Most frequent character per block

ValueCountFrequency (%)
02622
50.0%
11311
25.0%
.1311
25.0%

Cupper.Points
Real number (ℝ≥0)

Distinct26
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.502623951
Minimum6.5
Maximum8.5
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:47.609887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile6.83
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.5
Range2
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3524626271
Coefficient of variation (CV)0.04697858102
Kurtosis0.32317713
Mean7.502623951
Median Absolute Deviation (MAD)0.25
Skewness-0.1185075134
Sum9835.94
Variance0.1242299035
MonotocityNot monotonic
2021-02-14T15:04:47.714454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7.5184
14.0%
7.58136
10.4%
7.33114
 
8.7%
7.67113
 
8.6%
7.42103
 
7.9%
7.2585
 
6.5%
7.7584
 
6.4%
7.8381
 
6.2%
7.1763
 
4.8%
7.9252
 
4.0%
Other values (16)296
22.6%
ValueCountFrequency (%)
6.56
 
0.5%
6.586
 
0.5%
6.6720
1.5%
6.7514
1.1%
6.8321
1.6%
ValueCountFrequency (%)
8.58
 
0.6%
8.426
 
0.5%
8.338
 
0.6%
8.256
 
0.5%
8.1720
1.5%

Total.Cup.Points
Real number (ℝ≥0)

Distinct119
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.47691838
Minimum77.42
Maximum87.42
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:47.828968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum77.42
5-th percentile79.17
Q181.5
median82.5
Q383.58
95-th percentile85.33
Maximum87.42
Range10
Interquartile range (IQR)2.08

Descriptive statistics

Standard deviation1.768781819
Coefficient of variation (CV)0.02144577966
Kurtosis0.2375368529
Mean82.47691838
Median Absolute Deviation (MAD)1
Skewness-0.2290144227
Sum108127.24
Variance3.128589122
MonotocityNot monotonic
2021-02-14T15:04:47.945016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.595
 
7.2%
83.1738
 
2.9%
8337
 
2.8%
82.4231
 
2.4%
82.3329
 
2.2%
82.7529
 
2.2%
81.8326
 
2.0%
82.9226
 
2.0%
82.6726
 
2.0%
81.6725
 
1.9%
Other values (109)949
72.4%
ValueCountFrequency (%)
77.421
 
0.1%
77.51
 
0.1%
77.581
 
0.1%
77.671
 
0.1%
77.833
0.2%
ValueCountFrequency (%)
87.421
 
0.1%
87.331
 
0.1%
87.253
0.2%
87.172
0.2%
87.082
0.2%

Moisture
Real number (ℝ≥0)

Distinct12
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1113501144
Minimum0.05
Maximum0.16
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:48.038397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.11
median0.11
Q30.12
95-th percentile0.13
Maximum0.16
Range0.11
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01232852763
Coefficient of variation (CV)0.1107185897
Kurtosis6.038317189
Mean0.1113501144
Median Absolute Deviation (MAD)0
Skewness-0.7795584165
Sum145.98
Variance0.0001519925935
MonotocityNot monotonic
2021-02-14T15:04:48.125062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.11674
51.4%
0.12284
21.7%
0.1180
 
13.7%
0.1375
 
5.7%
0.0926
 
2.0%
0.1423
 
1.8%
0.0816
 
1.2%
0.058
 
0.6%
0.158
 
0.6%
0.067
 
0.5%
Other values (2)10
 
0.8%
ValueCountFrequency (%)
0.058
 
0.6%
0.067
 
0.5%
0.075
 
0.4%
0.0816
1.2%
0.0926
2.0%
ValueCountFrequency (%)
0.165
 
0.4%
0.158
 
0.6%
0.1423
 
1.8%
0.1375
 
5.7%
0.12284
21.7%

Category.One.Defects
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0.0
1311 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3933
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01311
100.0%
2021-02-14T15:04:48.296764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:48.347683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01311
100.0%

Most occurring characters

ValueCountFrequency (%)
02622
66.7%
.1311
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2622
66.7%
Other Punctuation1311
33.3%

Most frequent character per category

ValueCountFrequency (%)
02622
100.0%
ValueCountFrequency (%)
.1311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3933
100.0%

Most frequent character per script

ValueCountFrequency (%)
02622
66.7%
.1311
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3933
100.0%

Most frequent character per block

ValueCountFrequency (%)
02622
66.7%
.1311
33.3%

Quakers
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size10.4 KiB
0.0
1310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01310
99.9%
(Missing)1
 
0.1%
2021-02-14T15:04:48.496926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-14T15:04:48.548155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01310
100.0%

Most occurring characters

ValueCountFrequency (%)
02620
66.7%
.1310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2620
66.7%
Other Punctuation1310
33.3%

Most frequent character per category

ValueCountFrequency (%)
02620
100.0%
ValueCountFrequency (%)
.1310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3930
100.0%

Most frequent character per script

ValueCountFrequency (%)
02620
66.7%
.1310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3930
100.0%

Most frequent character per block

ValueCountFrequency (%)
02620
66.7%
.1310
33.3%

Category.Two.Defects
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.398932113
Minimum0
Maximum10
Zeros362
Zeros (%)27.6%
Memory size10.4 KiB
2021-02-14T15:04:48.606115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.442367533
Coefficient of variation (CV)1.018106148
Kurtosis0.9122287155
Mean2.398932113
Median Absolute Deviation (MAD)2
Skewness1.188133508
Sum3145
Variance5.965159164
MonotocityNot monotonic
2021-02-14T15:04:48.693257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0362
27.6%
2272
20.7%
1200
15.3%
3133
 
10.1%
4118
 
9.0%
573
 
5.6%
642
 
3.2%
739
 
3.0%
829
 
2.2%
922
 
1.7%
ValueCountFrequency (%)
0362
27.6%
1200
15.3%
2272
20.7%
3133
 
10.1%
4118
 
9.0%
ValueCountFrequency (%)
1021
1.6%
922
1.7%
829
2.2%
739
3.0%
642
3.2%

altitude_low_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct156
Distinct (%)14.4%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1326.835283
Minimum350
Maximum2285
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:48.794163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile758.75
Q11170
median1310.64
Q31550
95-th percentile1800
Maximum2285
Range1935
Interquartile range (IQR)380

Descriptive statistics

Standard deviation323.3443903
Coefficient of variation (CV)0.2436959541
Kurtosis0.2308307436
Mean1326.835283
Median Absolute Deviation (MAD)210.64
Skewness-0.3806061048
Sum1438289.447
Variance104551.5947
MonotocityNot monotonic
2021-02-14T15:04:49.224298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310.6492
 
7.0%
120080
 
6.1%
160065
 
5.0%
140059
 
4.5%
110055
 
4.2%
150054
 
4.1%
130048
 
3.7%
180041
 
3.1%
125038
 
2.9%
170036
 
2.7%
Other values (146)516
39.4%
(Missing)227
17.3%
ValueCountFrequency (%)
3503
0.2%
4002
0.2%
426.721
 
0.1%
4391
 
0.1%
4411
 
0.1%
ValueCountFrequency (%)
22851
0.1%
21362
0.2%
21121
0.1%
21001
0.1%
20801
0.1%

altitude_high_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct158
Distinct (%)14.6%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1368.512937
Minimum280
Maximum2285
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:49.340711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum280
5-th percentile774.15
Q11200
median1350
Q31600
95-th percentile1950
Maximum2285
Range2005
Interquartile range (IQR)400

Descriptive statistics

Standard deviation351.8316608
Coefficient of variation (CV)0.2570904895
Kurtosis0.06896512641
Mean1368.512937
Median Absolute Deviation (MAD)225.8152
Skewness-0.2683539902
Sum1483468.023
Variance123785.5175
MonotocityNot monotonic
2021-02-14T15:04:49.464198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135079
 
6.0%
120065
 
5.0%
140063
 
4.8%
110054
 
4.1%
150051
 
3.9%
180044
 
3.4%
130044
 
3.4%
170042
 
3.2%
125039
 
3.0%
160034
 
2.6%
Other values (148)569
43.4%
(Missing)227
 
17.3%
ValueCountFrequency (%)
2801
 
0.1%
3001
 
0.1%
3503
0.2%
426.721
 
0.1%
4391
 
0.1%
ValueCountFrequency (%)
22851
 
0.1%
22005
0.4%
21362
 
0.2%
21121
 
0.1%
21001
 
0.1%

altitude_mean_meters
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct167
Distinct (%)15.4%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1347.544516
Minimum350
Maximum2285
Zeros0
Zeros (%)0.0%
Memory size10.4 KiB
2021-02-14T15:04:49.582179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile800
Q11200
median1310.64
Q31600
95-th percentile1821.075
Maximum2285
Range1935
Interquartile range (IQR)400

Descriptive statistics

Standard deviation328.9097319
Coefficient of variation (CV)0.2440807914
Kurtosis0.09451253744
Mean1347.544516
Median Absolute Deviation (MAD)210.64
Skewness-0.3412730809
Sum1460738.255
Variance108181.6117
MonotocityNot monotonic
2021-02-14T15:04:49.702949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310.6492
 
7.0%
120066
 
5.0%
110052
 
4.0%
140052
 
4.0%
130050
 
3.8%
150044
 
3.4%
125039
 
3.0%
170036
 
2.7%
160035
 
2.7%
155034
 
2.6%
Other values (157)584
44.5%
(Missing)227
 
17.3%
ValueCountFrequency (%)
3503
 
0.2%
426.721
 
0.1%
4391
 
0.1%
4411
 
0.1%
44212
0.9%
ValueCountFrequency (%)
22851
0.1%
21362
0.2%
21121
0.1%
21001
0.1%
20801
0.1%

Interactions

2021-02-14T15:04:26.194583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.350456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.474783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.590288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.709644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.802765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.896080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:26.991435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.078612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.167523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.255067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.346859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.440220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.640932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.730812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.823438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:27.912349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.005401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.096442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.191213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.285281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.370635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.458005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.544903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.636786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.730468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.822512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:28.916670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.010705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.105110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.202513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.297931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.395853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.494352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.584572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.677117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.769321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.866100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:29.966478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.063254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.152787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.242495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.335875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.539277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.634875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.728547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.823130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.909445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:30.998820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.086767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.179191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.274132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.367040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.461957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.555575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.657662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.752329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.848769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:31.946858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.046528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.137254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.229857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.321835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.418692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.517294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.613663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.705197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.796364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.891404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:32.982694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.078223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.173553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.269456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.357124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.447137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.536385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.630438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.726614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.820976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:33.915141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.135342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.233612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.328077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.426286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.522178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.621032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.711291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.803981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.896331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:34.993427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.092132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.189049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.284327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.379021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.477804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.572802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.671722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.768029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.866842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:35.957448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.051049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.144254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.241456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.340630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.438410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.523179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.607747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.695716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.781172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.869547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:36.955752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.045384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.135144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.218170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.300851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.388576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.477851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.564786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.652784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.739567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.830158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:37.917358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.008054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.096656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.187092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.278461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.541402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.634310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.724617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.816448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.906317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:38.992867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.079926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.170802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.257380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.347601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.436029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.526248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.617153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.700346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.784921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.874319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:39.966912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.058996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.151084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.243323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.342873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.434722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.530653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.624948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.720877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.817425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.905926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:40.996248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.086902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.183968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.278185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.373038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.467813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.566143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.664779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.762805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.859170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:41.957406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.056998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.148218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.241103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.333533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.430431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.527379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.619945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.712173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.808093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.900484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:42.996458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.091070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.186965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.285160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.373693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.464256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.554356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-14T15:04:43.649152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-14T15:04:49.830826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-14T15:04:50.030728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-14T15:04:50.217779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-14T15:04:50.408722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-14T15:04:50.561773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-14T15:04:44.121122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-14T15:04:44.369060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-14T15:04:44.604635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-14T15:04:44.756035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country of OriginProducerNumber.of.BagsProcessing MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersCategory.Two.Defectsaltitude_low_metersaltitude_high_metersaltitude_mean_meters
0EthiopiaMETAD PLC300.0Washed / Wet7.587.587.427.57.507.5010.010.010.07.5082.50.120.00.00.01950.02200.02075.0
1EthiopiaMETAD PLC300.0Washed / Wet7.587.587.427.57.507.5010.010.010.07.5082.50.120.00.01.01950.02200.02075.0
2GuatemalaNaN5.0NaN7.587.587.427.57.507.5010.010.010.07.5082.50.110.00.00.01600.01800.01700.0
3EthiopiaYidnekachew Dabessa Coffee Plantation320.0Natural / Dry8.177.587.427.57.508.2510.010.010.07.5082.50.110.00.02.01800.02200.02000.0
4EthiopiaMETAD PLC300.0Washed / Wet7.587.587.427.57.508.3310.010.010.07.5082.50.120.00.02.01950.02200.02075.0
5BrazilNaN100.0Natural / Dry7.587.587.427.57.508.3310.010.010.08.3382.50.110.00.01.0NaNNaNNaN
6PeruHVC100.0Washed / Wet7.587.587.427.57.508.2510.010.010.08.5082.50.110.00.00.0NaNNaNNaN
7EthiopiaBazen Agricultural & Industrial Dev't Plc300.0NaN7.588.337.427.57.507.5010.010.010.07.5082.50.110.00.00.01570.01700.01635.0
8EthiopiaBazen Agricultural & Industrial Dev't Plc300.0NaN7.587.587.427.57.507.5010.010.010.07.5082.50.110.00.00.01570.01700.01635.0
9EthiopiaDiamond Enterprise Plc50.0Natural / Dry8.087.587.427.57.677.5010.010.010.08.5082.50.100.00.04.01795.01850.01822.5

Last rows

Country of OriginProducerNumber.of.BagsProcessing MethodAromaFlavorAftertasteAcidityBodyBalanceUniformityClean.CupSweetnessCupper.PointsTotal.Cup.PointsMoistureCategory.One.DefectsQuakersCategory.Two.Defectsaltitude_low_metersaltitude_high_metersaltitude_mean_meters
1301Mexicovarious small producers280.0Washed / Wet7.587.006.836.927.426.9210.010.010.06.7582.50.120.00.01.01000.001000.001000.00
1302BrazilNaN305.0Natural / Dry7.007.006.837.007.336.8310.010.010.06.6782.50.110.00.02.0NaNNaNNaN
1303HondurasOmar Acosta275.0Washed / Wet7.587.587.427.506.837.5010.010.010.07.5082.50.100.00.04.01450.001450.001450.00
1304HondurasOmar Acosta275.0Washed / Wet7.007.587.427.507.507.5010.010.010.06.5082.50.100.00.03.01450.001450.001450.00
1305HondurasOmar Acosta275.0Washed / Wet7.007.587.427.507.507.5010.010.010.07.5082.50.100.00.04.01450.001450.001450.00
1306MexicoJUAN CARLOS GARCÍA LOPEZ12.0Washed / Wet7.086.837.427.427.256.7510.010.010.06.7582.50.110.00.02.0900.00900.00900.00
1307HaitiCOEB Koperativ Ekselsyo Basen1.0Natural / Dry7.587.587.427.507.087.5010.010.010.07.5082.50.140.00.02.0350.00350.00350.00
1308NicaraguaTeófilo Narváez550.0Other7.257.587.427.507.507.5010.010.010.07.5082.50.130.00.05.01100.001100.001100.00
1309GuatemalaWILLIAM ESTUARDO MARTINEZ PACHECO275.0Washed / Wet7.507.587.427.677.337.5010.010.010.06.6782.50.100.00.04.01417.321417.321417.32
1310HondurasReinerio Zepeda275.0NaN7.587.587.427.507.507.5010.010.010.07.5082.50.120.00.02.01400.001400.001400.00